Iterative Bayesian Network Implementation by Using Annotated Association Rules

Fauré, Clément; Delprat, Sylvie; Boulicaut, Jean-François; Mille, Alain

doi:10.1007/11891451_29

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Jaroszewicz and Simovici [33] proposed finding the interestingness of frequent item sets using BNs as background knowledge. Fauré et al [34] proposed an iterative implementation of a knowledge model in a data mining context that relies on coupling a BN design with an association rule discovery technique. A novel idea of BN for significant feature selection is the Markov blanket (MB) [35], which is defined as the set of input features such that all other features are probabilistically independent of the target features.…”

Section: Ensemble Model For the Prediction Of Postoperative Morbiditymentioning

confidence: 99%

Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques

et al. 2010

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Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients' recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.

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Section: Ensemble Model For the Prediction Of Postoperative Morbiditymentioning

confidence: 99%

Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques

et al. 2010

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“…If we consider features of failure events as probabilistic variables, a Bayesian Network captures the conditional relations between those features over a set of events. In [4], Fauré C. et al used a five step algorithm to model the frequent association rules using Bayesian Networks. The first step of their algorithm is to create a Bayesian Network based on expert domain knowledge, and then compute association rules for all the combinations of features.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network representation of meaningful patterns in electricity distribution grids

Nemati

Sant'Anna

Nowaczyk

2016

2016 IEEE International Energy Conference (ENERGYCON)

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Abstract-The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur.In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way.

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